Spherical Graph Drawing by Multi-dimensional Scaling

We describe an efficient and scalable spherical graph embedding method. The method uses a generalization of the Euclidean stress function for Multi-Dimensional Scaling adapted to spherical space, where geodesic pairwise distances are employed instead of Euclidean distances. The resulting spherical s...

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Bibliographic Details
Published inarXiv.org
Main Authors Miller, Jacob, Huroyan, Vahan, Kobourov, Stephen
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.09.2022
Subjects
Embedding
Stress functions
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Summary:We describe an efficient and scalable spherical graph embedding method. The method uses a generalization of the Euclidean stress function for Multi-Dimensional Scaling adapted to spherical space, where geodesic pairwise distances are employed instead of Euclidean distances. The resulting spherical stress function is optimized by means of stochastic gradient descent. Quantitative and qualitative evaluations demonstrate the scalability and effectiveness of the proposed method. We also show that some graph families can be embedded with lower distortion on the sphere, than in Euclidean and hyperbolic spaces.
ISSN:2331-8422